mirror of
https://github.com/datahub-project/datahub.git
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f929190e6a
54.0.0: Filter removed documents during browse
53.0.15: Throwing 404, when no aspects found in DB for a given entity
53.0.14: add node label when updating relationship
53.0.8: Handle * character in the directory path for browse
53.0.4: apply label for add/update graph node&edge
53.0.0: META-10395: Don't package KafkaAuditHeader and UUID classes in mxe-avro
52.0.7: Add API in search DAO to support query filters
52.0.5: META-10073: Refactor remote DAO to use the new Ingest action method
51.0.16: allow query dao use default order by from neo4j
51.0.15: enable dataset indexing in graph
51.0.12: Move EMPTY_FILTER to RestliConstants
51.0.11: Add KafkaEventProducerFactory to utils
51.0.10: Create in-memory Neo4j in Neo4jDriverFactory if integration config is set
51.0.9: Generalized add() in BaseLocalDAO and EbeanLocalDAO
51.0.4: Move Neo4jTestServerBuilder to metadata test utils
51.0.3: Move makeRelationshipFilter to neo4j utils
50.0.7: Implement Neo4jDriverFactory
MP_VERSION=metadata-models:54.0.1
MP_VERSION=wherehows-samza:1.0.56
Data Hub
Introduction
Data Hub is Linkedin's generalized metadata search & discovery tool. To learn more about Data Hub, check out our Linkedin blog post and Strata presentation. This repository contains the complete source code to be able to build Data Hub's frontend & backend services.
Quickstart
- Install docker and docker-compose.
- Clone this repo and make sure you are at the
datahubbranch. - Run below command to download and run all Docker containers in your local:
cd docker/quickstart && docker-compose pull && docker-compose up --build
- After you have all Docker containers running in your machine, run below command to ingest provided sample data to Data Hub:
./gradlew :metadata-events:mxe-schemas:build && cd metadata-ingestion/mce-cli && pip install --user -r requirements.txt && python mce_cli.py produce -d bootstrap_mce.dat
Note: Make sure that you're using Java 8, we have a strict dependency to Java 8 for build.
- Finally, you can start
Data Hubby typinghttp://localhost:9001in your browser. You can sign in withdatahubas username and password.
Quicklinks
Roadmap
- Add user profile page
- Deploy Data Hub to Azure Cloud